it's seems that this function 'visualize_image' don't work when use commands

Hello
sorry,I have a problem about main_plot.pyI

the problem
FileNotFoundError: [Errno 2] No such file or directory: 'figs\Mnist/ratio0.5\Mnist-ratio0.5.png'

I hope to have a look during my busy schedule. I just touched this direction.Thank you!

when i'm ready to run "python main.py --dataset Mnist-alpha0.01-ratio0.05 --algorithm FedAvg --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3"I got the following problem。How can I solve it.

Average Global Accurancy = 0.0950, Loss = 2.31.
Traceback (most recent call last):
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\FLAlgorithms\users\userbase.py", line 163, in get_next_train_batch
(X, y) = next(self.iter_trainloader)
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 633, in next
data = self._next_data()
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 676, in _next_data
index = self._next_index() # may raise StopIteration
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 623, in _next_index
return next(self._sampler_iter) # may raise StopIteration
StopIteration

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\main.py", line 85, in
main(args)
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\main.py", line 42, in main
run_job(args, i)
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\main.py", line 37, in run_job
server.train(args)
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\FLAlgorithms\servers\serveravg.py", line 35, in train
user.train(glob_iter, personalized=self.personalized) #* user.train_samples
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\FLAlgorithms\users\useravg.py", line 23, in train
result =self.get_next_train_batch(count_labels=count_labels)
File "C:\kust\xuesu\code\FedGen-main\FedGen-main\FLAlgorithms\users\userbase.py", line 167, in get_next_train_batch
(X, y) = next(self.iter_trainloader)
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 633, in next
data = self._next_data()
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 676, in _next_data
index = self._next_index() # may raise StopIteration
File "C:\Users\Administrator\anaconda3\envs\FedGen\lib\site-packages\torch\utils\data\dataloader.py", line 623, in _next_index
return next(self._sampler_iter) # may raise StopIteration
StopIteration

Thank you for the great work.

Besides, Does anyone try to train with CIFAR-10. I have followed the setup for Mnist: replace the data loader of Mnist to CIFAR-10, change input dimension from 1 to 3, keep the same models. However, the result is not good (about 31%) on FedAvg.

Is there any special setting when do experiment with a new dataset? Thank you

It seems that the code does not supprt CUDA?

--device "cuda" can be set but it seems that it is always running on cpu

Thanks

It seems the code implemented does not conduct partial parameter sharing. As shown in line 103 of serverpFedGen.py, the partial parameter is default set to False, but in the paper, the pseudo-code shows only the classifier layer of the user's model is shared. Is it a bug or there is something I misunderstand in the code
self.aggregate_parameters()

Hi, I'm unable to run any of the files.
This was what is churned out. What does the Network configs: [6, 16, 'F'] mean?
python main.py --dataset Mnist-alpha0.1-ratio0.5 --algorithm FedDistll-FL --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3

Summary of training process:
Algorithm: FedDistll-FL
Batch size: 32
Learing rate : 0.01
Ensemble learing rate : 0.0001
Average Moving : 1.0
Subset of users : 10
Number of global rounds : 200
Number of local rounds : 20
Dataset : Mnist-alpha0.1-ratio0.5
Local Model : cnn
Device : cpu

     [ Start training iteration 0 ]

Creating model for mnist
Network configs: [6, 16, 'F']
Algorithm FedDistll-FL has not been implemented.

It seems that torch.rand generates [0,1) uniformly based on the official documentation instead of standard Gaussian. Is this intended? Thanks

I run the example experiment for FedGen on Mnist in README.md with the option "--device cuda" but find out there is no process deployed on GPU. I further explore your code and it seems that you have not handled "args.device" in all scripts. Besides, I add "os.environ["CUDA_VISIBLE_DEVICES"] = '0'" in main.py but the model is still deployed only on CPU. I wonder how I can utilize GPU for FedGen. I really appreciate your help!

Hi.

Does your implementation code of FedProx correspond to the algorithm block 2 in the original paper of FedProx? More specifically, the formula for updating lines 53-54 of code file "fedoptimizer.py" seems a little strange, right? In particular, what does lambda mean in FedProx algorithm?

The update formula I understand should be :
p.data=p.data - group['lr'] * ( p.grad. data + group ['mu'] * (p.data - pstar.data.clone())

Looking forward to your reply.

No issue.

The performance of FedAvg is not as good as FedGen simply because the Trainloader does not have a shuffle. After fixing the bugs Fedgen is not as effective as Fedavg.

If there are wrong tensor type errors when running experiments with FedGen algorithm, see changes in #3

Hi, Zhuang

can you share the script to generate the Celeb data? Thanks

这个循环里，user_output_logp参数第一次使用时是循环外47行定义的，接下来的循环，这个参数就是64行定义的
前者是本地训练的batch的label，后者是random choice的一个batch的label，这个是不是有点奇怪？

Thanks

Full error message: RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

Added the following as line 227 to serverbase.py to resolve:
test_losses = [t.detach() for t in test_losses]

Python version: 3.8.6

I think in the file plot_utils.py, the variable 'all_curves' used in the outside of the loop only saves the last algorithm's results, in this way, when we add several algorithms in the config, the plot figure result will cut the other algorithms' trend by following the last one's scope.

max_acc = np.max([max_acc, np.max(all_curves) ]) + 4e-2

python main_plot.py --dataset EMnist-alpha0.1-ratio0.1 --algorithms FedAvg,FedGen,FedProx,FedDistill --batch_size 32 --local_epochs 20 --num_users 10 --num_glob_iters 200 --plot_legend 1

Hi.

Does your implementation code of FedProx correspond to the algorithm block 2 in the original paper of FedProx? More specifically, the formula for updating lines 53-54 of code file "fedoptimizer.py" seems a little strange, right? In particular, what does lambda mean in FedProx algorithm?

The update formula I understand should be :
p.data=p.data - group['lr'] * ( p.grad. data + group ['mu'] * (p.data - pstar.data.clone())

Looking forward to your reply.

I wonder why the user_latent_loss is not mentioned in your paper.

Thank you for open-sourcing your project. I notice that "FedDF" (Ensemble Distillation for Robust Model Fusion in Federated Learning) is one of your baselines in your paper, however, you provide code for only FedAvg, FedProx, FedDistill, and FedGen. Could you please help me reproduce the results of FedDF? I really appreciate your help.

When I ran the EMNIST experiment after generation of emnist dataset I got:

(pt) wangshu@ubuntu:~/projects/FedGen$ CUDA_VISIBLE_DEVICES=3 python main.py --dataset EMnist-alpha0.1-ratio0.1 --algorithm FedGen --batch_size 32 --local_epochs 20 --num_users 10 --lamda 1 --model cnn --learning_rate 0.01 --personal_learning_rate 0.01 --num_glob_iters 200 --times 3 
================================================================================
Summary of training process:
Algorithm: FedGen
Batch size: 32
Learing rate       : 0.01
Ensemble learing rate       : 0.0001
Average Moving       : 1.0
Subset of users      : 10
Number of global rounds       : 200
Number of local rounds       : 20
Dataset       : EMnist-alpha0.1-ratio0.1
Local Model       : cnn
Device            : cpu
================================================================================


         [ Start training iteration 0 ]           


Creating model for emnist
Network configs: [6, 16, 'F']
Dataset emnist
/home/wangshu/miniconda3/envs/pt/lib/python3.9/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
  warnings.warn(warning.format(ret))
Build layer 57 X 256
Build last layer 256 X 32
ensemble_lr: 0.0001
ensemble_batch_size: 128
unique_labels: 25
latent_layer_idx: -1
label embedding 0
ensemeble learning rate: 0.0001
ensemeble alpha = 1, beta = 0, eta = 1
generator alpha = 10, beta = 1
Number of Train/Test samples: 12480 8120
Data from 20 users in total.
Finished creating FedAvg server.


-------------Round number:  0  -------------


Traceback (most recent call last):
  File "/home/wangshu/projects/FedGen/main.py", line 85, in <module>
    main(args)
  File "/home/wangshu/projects/FedGen/main.py", line 42, in main
    run_job(args, i)
  File "/home/wangshu/projects/FedGen/main.py", line 37, in run_job
    server.train(args)
  File "/home/wangshu/projects/FedGen/FLAlgorithms/servers/serverpFedGen.py", line 78, in train
    self.evaluate()
  File "/home/wangshu/projects/FedGen/FLAlgorithms/servers/serverbase.py", line 226, in evaluate
    test_ids, test_samples, test_accs, test_losses = self.test(selected=selected)
  File "/home/wangshu/projects/FedGen/FLAlgorithms/servers/serverbase.py", line 165, in test
    ct, c_loss, ns = c.test()
  File "/home/wangshu/projects/FedGen/FLAlgorithms/users/userbase.py", line 137, in test
    loss += self.loss(output, y)
  File "/home/wangshu/miniconda3/envs/pt/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/wangshu/miniconda3/envs/pt/lib/python3.9/site-packages/torch/nn/modules/loss.py", line 216, in forward
    return F.nll_loss(input, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction)
  File "/home/wangshu/miniconda3/envs/pt/lib/python3.9/site-packages/torch/nn/functional.py", line 2388, in nll_loss
    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 25 is out of bounds.
(pt) wangshu@ubuntu:~/projects/FedGen$ 

Pythorch 1.8.1, python 3.9.4.